A machine learning algorithm run on non-representative input could be said to lack experience with the portion of input that isn't represented in the training set.

E.g. I think it's pretty obvious that machine learning algorithms exhibit far more vernacular-bias than purely logical reasoning techniques.

> I made this claim given that I explicitly listed two examples of algorithms which are biased.

Your parent is pointing out the very real possibility that technically-unbiased algorithms can produce vernacularly-biased outputs. I.e., that statistic's definition of "bias" does not sufficiently capture the notion of bias as it's used in the vernacular.

I don't think you've refuted that claim.

At the end of the day, your approach toward side-stepping the issue of vernacular bias is intellectually lazy. Instead of tackling the problem head on, you're hiding behind a mathematical object that happens to have the same name as the actual thing under discussion.

> I'm beginning to think you are seeking to derail the conversation rather than discussing statistics in good faith.

I think your characterization of bias in terms of statistic's technical definition is already skimming the edges of good faith. You know what the reporter means when they say "bias", and that's not the meaning that statisticians use in technical settings.